The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

Persistent Biomechanical Alterations After ACL Reconstruction Are Associated With Early Cartilage Matrix Changes Detected by Quantitative MR.

BackgroundThe effectiveness of anterior cruciate ligament (ACL) reconstruction in preventing early osteoarthritis is debated. Restoring the original biomechanics may potentially prevent degeneration, but apparent pathomechanisms have yet to be described. Newer quantitative magnetic resonance (qMR) imaging techniques, specifically T1ρ and T2, offer novel, noninvasive methods of visualizing and quantifying early cartilage degeneration.PurposeTo determine the tibiofemoral biomechanical alterations before and after ACL reconstruction using magnetic resonance imaging (MRI) and to evaluate the association between biomechanics and cartilage degeneration using T1ρ and T2.Study designCohort study; Level of evidence, 2.MethodsKnee MRIs of 51 individuals (mean age, 29.5 ± 8.4 years) with unilateral ACL injuries were obtained prior to surgery; 19 control subjects (mean age, 30.7 ± 5.3 years) were also scanned. Follow-up MRIs were obtained at 6 months and 1 year. Tibial position (TP), internal tibial rotation (ITR), and T1ρ and T2 were calculated using an in-house Matlab program. Student t tests, repeated measures, and regression models were used to compare differences between injured and uninjured sides, observe longitudinal changes, and evaluate correlations between TP, ITR, and T1ρ and T2.ResultsTP was significantly more anterior on the injured side at all time points (P &lt; .001). ITR was significantly increased on the injured side prior to surgery (P = .033). At 1 year, a more anterior TP was associated with elevated T1ρ (P = .002) and T2 (P = .026) in the posterolateral tibia and with decreased T2 in the central lateral femur (P = .048); ITR was associated with increased T1ρ in the posteromedial femur (P = .009). ITR at 6 months was associated with increased T1ρ at 1 year in the posteromedial tibia (P = .029).ConclusionPersistent biomechanical alterations after ACL reconstruction are related to significant changes in cartilage T1ρ and T2 at 1 year postreconstruction. Longitudinal correlations between ITR and T1ρ suggest that these alterations may be indicative of future cartilage injury, leading to degeneration and osteoarthritis.Clinical relevanceNewer surgical techniques should be developed to eliminate the persistent anterior tibial translation commonly seen after ACL reconstruction. qMR will be a useful tool to evaluate the ability of these newer techniques to prevent cartilage changes

Quantifying the Tibiofemoral Joint Space Using X-ray Tomosynthesis

Purpose: Digital x-ray tomosynthesis (DTS) has the potential to provide 3D information about the knee joint in a load-bearing posture, which may improve diagnosis and monitoring of knee osteoarthritis compared with projection radiography, the current standard of care. Manually quantifying and visualizing the joint space width (JSW) from 3D tomosynthesis datasets may be challenging. This work developed a semiautomated algorithm for quantifying the 3D tibiofemoral JSW from reconstructed DTS images. The algorithm was validated through anthropomorphic phantom experiments and applied to three clinical datasets. Methods: A user-selected volume of interest within the reconstructed DTS volume was enhanced with 1D multiscale gradient kernels. The edge-enhanced volumes were divided by polarity into tibial and femoral edge maps and combined across kernel scales. A 2D connected components algorithm was performed to determine candidate tibial and femoral edges. A 2D joint space width map (JSW) was constructed to represent the 3D tibiofemoral joint space. To quantify the algorithm accuracy, an adjustable knee phantom was constructed, and eleven posterior–anterior (PA) and lateral DTS scans were acquired with the medial minimum JSW of the phantom set to 0–5 mm in 0.5 mm increments (VolumeRadTM, GE Healthcare, Chalfont St. Giles, United Kingdom). The accuracy of the algorithm was quantified by comparing the minimum JSW in a region of interest in the medial compartment of the JSW map to the measured phantom setting for each trial. In addition, the algorithm was applied to DTS scans of a static knee phantom and the JSW map compared to values estimated from a manually segmented computed tomography (CT) dataset. The algorithm was also applied to three clinical DTS datasets of osteoarthritic patients. Results: The algorithm segmented the JSW and generated a JSW map for all phantom and clinical datasets. For the adjustable phantom, the estimated minimum JSW values were plotted against the measured values for all trials. A linear fit estimated a slope of 0.887 (R2¼0.962) and a mean error across all trials of 0.34 mm for the PA phantom data. The estimated minimum JSW values for the lateral adjustable phantom acquisitions were found to have low correlation to the measured values (R2¼0.377), with a mean error of 2.13 mm. The error in the lateral adjustable-phantom datasets appeared to be caused by artifacts due to unrealistic features in the phantom bones. JSW maps generated by DTS and CT varied by a mean of 0.6 mm and 0.8 mm across the knee joint, for PA and lateral scans. The tibial and femoral edges were successfully segmented and JSW maps determined for PA and lateral clinical DTS datasets. Conclusions: A semiautomated method is presented for quantifying the 3D joint space in a 2D JSW map using tomosynthesis images. The proposed algorithm quantified the JSW across the knee joint to sub-millimeter accuracy for PA tomosynthesis acquisitions. Overall, the results suggest that x-ray tomosynthesis may be beneficial for diagnosing and monitoring disease progression or treatment of osteoarthritis by providing quantitative images of JSW in the load-bearing knee

Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

Quantitative Magnetic Resonance Imaging and Analysis of Articular Cartilage and Osteoarthritis

MRI plays an important role in the continuing search for a sensitive osteoarthritis (OA) imaging biomarker able to detect early, pre-morphological alterations in cartilage composition. Determining the compositional recovery pattern of cartilage following acute joint loading could potentially present a more sensitive biomarker for defining cartilage health [1]. However, only a limited amount of studies have assessed both the immediate effect of joint loading on cartilage, as well as its post-loading recovery. In addition, when assessing the compositional responses of cartilage to joint loading, previous studies usually did not incorporate the measurement error of the used quantitative MRI technique into their analysis. Therefore, an uncertainty persists whether or not compositional MRI techniques are sensitive enough to measure changes in water and macromolecular content of cartilage, or if previous studies were merely measuring noise. Consequently, an objective of this thesis is to increase our understanding of and reliability in quantitative T2 and T1ρ relaxation time mapping to detect compositional responses of cartilage following a joint loading activity. Furthermore, to obtain the quantitative morphological and compositional measures of cartilage, detailed region-specific delineation of cartilage is required. This delineation (or segmentation) of cartilage is laborious and time-consuming as it is usually performed manually by an expert observer. Many new advances in image analysis, particularly those in convolutional neural networks (CNNs) and deep learning, have enabled a time-efficient semi- or fully-automated alternative to this process [2, 3]. This thesis explores the utility of deep CNNs generated segmentations for accurate surface-based analysis of cartilage morphology and composition from knee MRIs as well as of cortical bone thickness from knee CTs. Chapter 1 will provide an introduction into the structure and biomechanics of articular cartilage and the role of MRI in imaging the degenerative joint disorder, osteoarthritis as well as the effects of different joint loading activities on cartilage morphology and composition. Chapter 2 explains the principle of MRI and the pulse sequences used in the following chapter for the morphometric and compositional assessment of articular cartilage. Chapter 3 describes the use of 3D Cartilage Surface Mapping (3D-CaSM) [3] to assess variations in cartilage T1ρ and T2 relaxation times of young, healthy participants following a mild, unilateral stepping activity. By evaluating and incorporating the intrasessional repeatability of the T1ρ and T2 mapping techniques, I aim to highlight those cartilage areas experiencing exercise-induced compositional changes greater than measurement error. A significant amount of time is needed to manually segment the regions-of-interest required to perform the 3D-CaSM used in Chapter 3. Therefore, in Chapter 4, I assessed the use of deep convolutional neural networks for automating the segmentation process for multiple knee joint tissues simultaneous and increase the time-efficiency for evaluating knee MR datasets. I evaluated the use of a conditional Generative Adversarial Network (cGAN) as a potentially improved method for automated segmentation compared to the widely used convolutional neural network, U-Net. In Chapter 5 I combined the 3D-CaSM and automated segmentation methods presented in Chapters 3 and 4, respectively to assess the use of fully automatic segmentations of femoral and tibial bone-cartilage structures for accurate surface-based analysis of cartilage morphology and composition on knee MR images. This was performed on publicly available data from the Osteoarthritis Initiative, a multicentre observational study with expert manual segmentations provided by the Zuse Institute in Berlin. Chapter 6 describes an automated pipeline for subchondral cortical bone thickness mapping from knee CT data. I developed a method of using automated segmentations of articular cartilage and bone from knee MRI data to determine the periarticular bone surface which is covered by cartilage. This surface was then used to perform cortical bone thickness measurements on corresponding CT data. I validated this pipeline using data from the EU-funded, multi-centre observational study called Applied Private-Public partneRship enabling OsteoArthritis Clinical Headway (APPROACH). Chapter 7 summarises the main conclusions and contributions of the works presented in this thesis as well as providing directions for future work.PhD Studentship funded by GlaxoSmithKlin

Analyzing Femorotibial Cartilage Thickness Using Anatomically Standardized Maps: Reproducibility and Reference Data.

Alterations in cartilage thickness (CTh) are a hallmark of knee osteoarthritis, which remain difficult to characterize at high resolution, even with modern magnetic resonance imaging (MRI), due to a paucity of standardization tools. This study aimed to assess a computational anatomy method producing standardized two-dimensional femorotibial CTh maps. The method was assessed with twenty knees, processed following three common experimental scenarios. Cartilage thickness maps were obtained for the femorotibial cartilages by reconstructing bone and cartilage mesh models in tree-dimension, calculating three-dimensional CTh maps, and anatomically standardizing the maps. The intra-operator accuracy (median (interquartile range, IQR) of -0.006 (0.045) mm), precision (0.152 (0.070) mm), entropy (7.02 (0.71) and agreement (0.975 (0.020))) results suggested that the method is adequate to capture the spatial variations in CTh and compare knees at varying osteoarthritis stages. The lower inter-operator precision (0.496 (0.132) mm) and agreement (0.808 (0.108)) indicate a possible loss of sensitivity to detect differences in a setting with multiple operators. The results confirmed the promising potential of anatomically standardized maps, with the lower inter-operator reproducibility stressing the need to coordinate operators. This study also provided essential reference data and indications for future research using CTh maps